Triple
T15313830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Very Deep Convolutional Networks for Large-Scale Image Recognition |
E366102
|
entity |
| Predicate | shows |
P2371
|
FINISHED |
| Object | VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication |
E366102
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication | Statement: [Very Deep Convolutional Networks for Large-Scale Image Recognition, shows, VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication Context triple: [Very Deep Convolutional Networks for Large-Scale Image Recognition, shows, VGG-16 and VGG-19 achieve state-of-the-art performance on ImageNet at the time of publication]
-
A.
Very Deep Convolutional Networks for Large-Scale Image Recognition
chosen
"Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.
-
B.
ImageNet Classification with Deep Convolutional Neural Networks
"ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
-
C.
ImageNet CNN
ImageNet CNN is a convolutional neural network model trained on the large-scale ImageNet dataset, commonly used as a powerful pretrained feature extractor for various computer vision tasks.
-
D.
Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
-
E.
VGG
VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd050108190a584543cb93943a4 |
completed | April 16, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b3dac6c81908eaa0aaad29113ee |
completed | May 9, 2026, 10:23 a.m. |
Created at: April 10, 2026, 3:16 a.m.